A mesh-free method for interface problems using the deep learning approach
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2019
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| _version_ | 1866912161422376960 |
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| author | Wang, Zhongjian Zhang, Zhiwen |
| author_facet | Wang, Zhongjian Zhang, Zhiwen |
| contents | In this paper, we propose a mesh-free method to solve interface problems using the deep learning approach. Two interface problems are considered. The first one is an elliptic PDE with a discontinuous and high-contrast coefficient. While the second one is a linear elasticity equation with discontinuous stress tensor. In both cases, we formulate the PDEs into variational problems, which can be solved via the deep learning approach. To deal with the inhomogeneous boundary conditions, we use a shallow neuron network to approximate the boundary conditions. Instead of using an adaptive mesh refinement method or specially designed basis functions or numerical schemes to compute the PDE solutions, the proposed method has the advantages that it is easy to implement and mesh-free. Finally, we present numerical results to demonstrate the accuracy and efficiency of the proposed method for interface problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1901_00618 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | A mesh-free method for interface problems using the deep learning approach Wang, Zhongjian Zhang, Zhiwen Computational Physics Numerical Analysis 35J20, 35R05, 65N30, 68T99, 74B05 In this paper, we propose a mesh-free method to solve interface problems using the deep learning approach. Two interface problems are considered. The first one is an elliptic PDE with a discontinuous and high-contrast coefficient. While the second one is a linear elasticity equation with discontinuous stress tensor. In both cases, we formulate the PDEs into variational problems, which can be solved via the deep learning approach. To deal with the inhomogeneous boundary conditions, we use a shallow neuron network to approximate the boundary conditions. Instead of using an adaptive mesh refinement method or specially designed basis functions or numerical schemes to compute the PDE solutions, the proposed method has the advantages that it is easy to implement and mesh-free. Finally, we present numerical results to demonstrate the accuracy and efficiency of the proposed method for interface problems. |
| title | A mesh-free method for interface problems using the deep learning approach |
| topic | Computational Physics Numerical Analysis 35J20, 35R05, 65N30, 68T99, 74B05 |
| url | https://arxiv.org/abs/1901.00618 |